As AWS CEO Andy Jassy put it while introducing the new service on stage at re:invent, “Amazon SageMaker, an easy way to train, deploy machine learning models for every day developers.” ...

Deploying this house price prediction machine learning model on an Amazon EC2 instance as web application with Flask which makes the model accessible and scalable for real-time prediction queries. The ...

The benefits of machine learning ... ML training deployment, and then scale those deployments into clusters. The guide, titled “Getting started with a ML training model using AWS & PyTorch ...

That involves AWS building the machine learning infrastructure so customers can focus ... it even easier for teams to expedite the end-to-end development and deployment of ML models. From ...

This repo contains all the code needed to run, build and deploy Cartoonify: a toy app I made from ... etc.). Since I frequently build machine learning models and integrate them into web applications, ...

Use precise geolocation data. Actively scan device characteristics for identification. Store and/or access information on a ...

Swami Sivasubramanian at the AWS keynote ... machine learning challenges for governance, Amazon is launching three new capabilities for SageMaker – ML Governance Role Manager, Model Cards ...

Machine learning (ML) and high-performance computing (HPC) on AWS run compute-intensive workloads across ... Next, you’ll learn how to build, train, and deploy large models using distributed training.

Cloudian has announced an open-source software contribution that fuses PyTorch, the widely acclaimed machine learning library, with local Cloudian HyperStore S3-compatible storage solutions.